CN110221173B - Power distribution network intelligent diagnosis method based on big data drive - Google Patents
Power distribution network intelligent diagnosis method based on big data drive Download PDFInfo
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- CN110221173B CN110221173B CN201910535174.0A CN201910535174A CN110221173B CN 110221173 B CN110221173 B CN 110221173B CN 201910535174 A CN201910535174 A CN 201910535174A CN 110221173 B CN110221173 B CN 110221173B
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Abstract
The invention discloses a power distribution network intelligent diagnosis method based on big data drive, which is characterized in that the maximum composite probability of faults of each section is finally obtained through incomplete historical monitoring data of each section of a power distribution network by a K-means hierarchical evaluation algorithm according to the sequence of a basic layer step, an intermediate layer step and an output layer step, so that the fragile section of the power distribution network can be effectively found, and the real-time running state of the power distribution network can be scientifically evaluated.
Description
Technical Field
The invention relates to a power distribution network intelligent diagnosis method based on big data driving, which is used in the field of intelligent power grids.
Background
The distribution network is directly connected with the user, meanwhile, due to the characteristics of wide geographical distribution coverage area, complex and changeable geographical environment, high fault rate and the like, the power distribution network can quickly and accurately sense and diagnose the fault state of the distribution network, and the power distribution network is an indispensable technical measure for improving the power supply safety and reliability of the user.
The accumulation of a large amount of real-time data and fault record historical data from the power distribution network provides a good data base for intelligent evaluation of the power distribution network. However, the amount of such data is too large, and there are errors and inconsistencies, and the number of data monitoring points of the power distribution network is limited, so that massive data cannot be effectively applied. Analyzing the internal structure of the data, the content of data transmission and the incidence relation among the data, combing the data, performing system analysis and diagnosis on various attributes of the data, ensuring the quality of the provided data, improving the effectiveness of the data in application, and ensuring the practicability, effectiveness and rationality of the data in the application process are the main targets of technicians.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a power distribution network intelligent diagnosis method based on big data drive, which can realize intelligent diagnosis of a power distribution network.
One technical method for achieving the above purpose is as follows: a power distribution network intelligent diagnosis method based on big data drive, based on the sensing and diagnosis of the abnormal and fault of the key composition unit of the power distribution network of incomplete information, adopts K-means hierarchical evaluation strategy to carry out unsupervised classification on the incomplete historical monitoring information of the local section of the power distribution network, and evaluates the fault probability of each evaluation section in each cluster according to the historical fault record information, and comprises the following steps:
step 1, a basic layer step, wherein the basic layer adopts a K-means clustering algorithm to perform unsupervised classification on incomplete historical monitoring information of local sections, calculates the fault probability of each evaluation section in each cluster according to historical fault recording information, and divides m pieces of historical data information into K different classes by using the K-means algorithm, wherein the fault probability in the first class can be obtained
The probability of failure of the jth evaluation section in the class;
step 2, the intermediate layer provides a section composite fault probability solving algorithm, the probability that the real-time monitoring data belong to each cluster is calculated according to the distance from the real-time sampling data matrix of the monitoring section to each cluster, and the composite probability of the fault of each section is obtained by combining the probability of the fault of each section in each cluster;
and 3, outputting a layer, wherein the output layer provides a maximum composite probability evaluation algorithm, and the maximum composite probability of faults of each section is used as an overall operation state evaluation coefficient to evaluate the operation state grade of the power distribution network.
Further, the intermediate layer step, the section composite fault probability solving algorithm is based on the real-time data of the monitoring section, and the real-time data and the first fault probability are calculated
Distance of cluster centers. Then, the probability coefficients of the current real-time monitoring data belonging to different clusters can be obtained, and the calculation process is as follows:
defining an inverse distance coefficient:
in the formula (I), the compound is shown in the specification,
is a cluster number, and is a cluster number,
for monitoring data in real time to
The distance between the centers of the clusters is,
is an inverse distance coefficient;
calculating the real-time monitoring data
Probability coefficient of cluster:
in the formula, k represents the maximum cluster number,
indicating that the real-time monitoring data belongs to
Probability coefficients of clusters;
according to the first
The probability of the fault of the jth section in the cluster and the monitoring data belong to the
The probability coefficient of the cluster can calculate the composite fault probability of the real-time monitoring data in each evaluation section:
further, a point with the maximum composite fault probability is taken to represent the overall operation state of the power distribution network:
according to the intelligent diagnosis method for the power distribution network based on big data drive, the maximum composite probability of faults of each section is finally obtained through incomplete historical monitoring data of each section of the power distribution network according to the sequence of the steps of the basic layer, the intermediate layer and the output layer through a K-means hierarchical evaluation algorithm, so that the fragile section of the power distribution network can be effectively found, and the real-time running state of the power distribution network can be scientifically evaluated.
Detailed Description
In order to better understand the technical process of the invention, the following is described in detail by means of specific examples:
the invention discloses a power distribution network intelligent diagnosis method based on big data drive, which is based on the sensing and diagnosis of the abnormity and the fault of a key component unit of a power distribution network of incomplete information, adopts a K-means hierarchical evaluation strategy to carry out unsupervised classification on incomplete historical monitoring information of local sections of the power distribution network, and evaluates the fault probability of each evaluation section in each cluster according to historical fault record information, and comprises the following steps:
step 1, base layer step, base layerUnsupervised classification is carried out on incomplete historical monitoring information of local sections by adopting a K-means clustering algorithm, the fault probability of each evaluation section in each cluster is calculated according to historical fault record information, m pieces of historical data information are divided into K different classes by utilizing the K-means algorithm, and the fault probability in the first class can be obtained
The probability of failure of the jth evaluation section in the class;
step 2, the intermediate layer provides a section composite fault probability solving algorithm, the probability that the real-time monitoring data belong to each cluster is calculated according to the distance from the real-time sampling data matrix of the monitoring section to each cluster, and the composite probability of the fault of each section is obtained by combining the probability of the fault of each section in each cluster;
the section composite fault probability solving algorithm is based on real-time data of the monitored sections, and calculates the real-time data and the first data
Distance of cluster centers. Then, the probability coefficients of the current real-time monitoring data belonging to different clusters can be obtained, and the calculation process is as follows:
defining an inverse distance coefficient:
in the formula (I), the compound is shown in the specification,
is a cluster number, and is a cluster number,
for monitoring data in real time to
The distance between the centers of the clusters is,
is an inverse distance coefficient;
calculating the real-time monitoring data
Probability coefficient of cluster:
in the formula, k represents the maximum cluster number,
indicating that the real-time monitoring data belongs to
Probability coefficients of clusters;
according to the first
The probability of the fault of the jth section in the cluster and the monitoring data belong to the
The probability coefficient of the cluster can calculate the composite fault probability of the real-time monitoring data in each evaluation section:
and 3, outputting a layer, wherein the output layer provides a maximum composite probability evaluation algorithm, the maximum composite probability of faults in each section is used as an evaluation coefficient of the overall operation state, the operation state grade of the power distribution network is evaluated, and the point with the maximum composite fault probability is taken to represent the overall operation state of the power distribution network:
the method is based on the voltage information of the limited monitoring points, and utilizes a K-means hierarchical evaluation algorithm to evaluate the state of all evaluation sections of the power distribution network under study. And sensing the time of the power distribution network fault by utilizing the ring theorem and the average spectrum radius characteristic based on the voltage and current redundant information of the limited monitoring point or the full monitoring point. According to an automatic system from a local dispatching, characteristic analysis, data cleaning and extraction are carried out on data, then classification and combination are carried out on multi-source heterogeneous data by comprehensively considering data pre-set rules based on different dimensions such as data sources, purposes, data attributes and the like, and a high-reliability state perception and diagnosis method for the new energy-containing power distribution network is established. The output of the method is the sensing and diagnosis result of the fault state of the power distribution network.
It should be understood by those skilled in the art that the above embodiments are only for illustrating the present invention and are not to be used as a limitation of the present invention, and that changes and modifications to the above described embodiments are within the scope of the claims of the present invention as long as they are within the spirit and scope of the present invention.
Claims (3)
1. A power distribution network intelligent diagnosis method based on big data drive is characterized by comprising the following steps of based on sensing and diagnosis of abnormity and faults of key component units of a power distribution network of incomplete information, adopting a K-means hierarchical evaluation strategy to carry out unsupervised classification on incomplete historical monitoring information of local sections of the power distribution network, and evaluating fault probability of each evaluation section in each cluster according to historical fault record information:
step 1, a basic layer step, wherein the basic layer adopts a K-means clustering algorithm to perform unsupervised classification on incomplete historical monitoring information of local sections, calculates the fault probability of each evaluation section in each cluster according to historical fault recording information, and divides m pieces of historical data information into K different classes by using the K-means algorithm, wherein the fault probability in the first class can be obtained
Probability of failure of jth evaluation section in class
Step 2, the intermediate layer provides a section composite fault probability solving algorithm, the probability that the real-time monitoring data belong to each cluster is calculated according to the distance from the real-time sampling data matrix of the monitoring section to each cluster, and the composite probability of the fault of each section is obtained by combining the probability of the fault of each section in each cluster;
and 3, outputting a layer, wherein the output layer provides a maximum composite probability evaluation algorithm, and the maximum composite probability of faults of each section is used as an overall operation state evaluation coefficient to evaluate the operation state grade of the power distribution network.
2. The intelligent diagnosis method for the power distribution network based on big data driving as claimed in claim 1, wherein the intermediate layer step, section composite fault probability solving algorithm, is based on real-time data of the monitored section, and calculates the real-time data and the first real-time data
Distance of cluster centers; then, the probability coefficients of the current real-time monitoring data belonging to different clusters can be obtained, and the calculation process is as follows:
defining an inverse distance coefficient:
in the formula (I), the compound is shown in the specification,
is a cluster number, and is a cluster number,
for monitoring data in real time to
The distance between the centers of the clusters is,
is an inverse distance coefficient;
calculating the real-time monitoring data
Probability coefficient of cluster:
in the formula, k represents the maximum cluster number,
indicating that the real-time monitoring data belongs to
Probability coefficients of clusters;
according to the first
The probability of the fault of the jth section in the cluster and the monitoring data belong to the
The probability coefficient of the cluster can calculate the composite fault probability of the real-time monitoring data in each evaluation section:
3. the intelligent diagnosis method for the power distribution network based on big data driving according to claim 1, characterized by representing the overall operation state of the power distribution network by taking the point with the maximum composite fault probability:
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| CN112180215A (en) * | 2020-09-29 | 2021-01-05 | 国网上海市电力公司 | Intelligent power distribution network fault searching and positioning method based on multi-source data fusion |
| CN112180216A (en) * | 2020-09-29 | 2021-01-05 | 国网上海市电力公司 | Power distribution network intelligent sensing and diagnosis method based on big data drive |
| CN113673904A (en) * | 2021-08-31 | 2021-11-19 | 江苏省电力试验研究院有限公司 | Data-driven power distribution network user-changing relation diagnosis method and equipment |
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